Responsibly Emboldening Spatially Dependent Predictions

Music City Center 

Boldness-recalibration enables better decision via responsible emboldening of probability predictions under the assumption the predictions are independent.  However, many scenarios involve probability predictions with spatial dependencies, such as election modeling, weather forecasting, species occurrence modeling, and more. In this presentation, we extend boldness-recalibration towards spatial calibration of probability predictions of this nature.  We demonstrate how to leverage spatial relationships to responsibly embolden probability predictions, while maintaining a user-specified probability of calibration. We compare the efficacy of boldness-recalibration with and without explicitly accounting for spatial dependencies.